Exploring the relationship between nighttime light and land use is of great significance to understanding human nighttime activities and studying socioeconomic phenomena. Models have been studied to explain the relationships, but the existing studies seldom consider the spatial autocorrelation of night light data, which leads to large regression residuals and an inaccurate regression correlation between night light and land use. In this paper, two non-negative spatial autoregressive models are proposed for the spatial lag model and spatial error model, respectively, which use a spatial adjacency matrix to calculate the spatial autocorrelation effect of light in adjacent pixels on the central pixel. The application scenarios of the two models were analyzed, and the contribution of various land use types to nighttime light in different study areas are further discussed. Experiments in Berlin, Massachusetts and Shenzhen showed that the proposed methods have better correlations with the reference data compared with the non-negative least-squares method, better reflecting the luminous situation of different land use types at night. Furthermore, the proposed model and the obtained relationship between nighttime light and land use types can be utilized for other applications of nighttime light images in the population, GDP and carbon emissions for better exploring the relationship between nighttime remote sensing brightness and socioeconomic activities.

Urban heat island (UHI) effect tends to harm health, increase anthropogenic energy consumption, and water consumption. Some policies targeting UHI mitigation have been implemented for a few years and thus needs to be evaluated for changes or modifications in the future. A low-cost approach to rapidly monitoring UHI intensity variations can assist in evaluating policy implementations. In this study, we proposed a new approach to local-scale UHI intensity estimates by using nighttime light satellite imageries. We explored to what extent UHI intensity could be estimated according to nighttime light intensity at two local scales. We attempted to estimate district-level and neighbourhood-level UHI intensity across London and Paris. As the geography level rises from district to neighbourhood, the capacity of the models explaining the variations of the UHI intensity decreases. Although the possible presence of residual spatial autocorrelation in the conventional regression models applied to geospatial data, most of the studies are likely to neglect this issue when fitting data to models. To remove negative effects of the residual spatial autocorrelation, this study used spatial regression models instead of non-spatial regression models (e.g., OLS models) to estimate UHI intensity. As a result, district-level UHI intensity was successfully estimated according to nighttime light intensity (approximately R2 = 0.7, MAE =1.16 °C, and RMSE =1.74 °C).

Lunar sun-reflected light can be effectively measured through a low-light band or a day/night band (DNB) implemented on space-based optical sensors. Based on moonlight, nocturnal observations for artificial light sources at night can be achieved. However, to date, an open-sourced and mature Low-Light Radiative Transfer Model (LLRTM) for the further understanding of the radiative transfer problem at night is still unavailable. Therefore, this study develops a new LLRTM at night with the correction of the lunar and active surface light sources. First, the radiative transfer equations with an active surface light source are derived for the calculation based on the lunar spectral irradiance (LSI) model. The simulation from this new LLRTM shows a minimal bias when compared with the discrete ordinates radiative transfer (DISORT) model. The simulated results of radiance and reflectance at the top of the atmosphere (TOA) also show that the surface light source has a remarkable impact on the radiative transfer process. In contrast, the change in the lunar phase angle has minimal influence. Also, comparing with space-based DNB radiance observations, LLRTM shows the potential to simulate space-based low-light imager observations under an effective surface light source condition during the night.

In recent decades, there has been an increase in artificial lighting in the world due to urbanization and the revolution of LED lighting. Artificial lighting is an indicator of human activity, but can adversely affect natural ecosystems and people due to negative impacts of light pollution. Space-borne and airborne imagery as well as ground-based measurements enable to measure the intensity and spectra of artificial lights. One of the challenges in remote sensing of night-time lights is how to ground truth night-time imagery acquired by satellites, and how much do space-borne measurements represent the brightness as perceived by organisms. Most of the studies on night-time lights to-date were done using panchromatic sensors at large spatial extents, which did not allow to examine intra-urban variation in night light intensity and spectra. The aim of this study was to test the capability of the new Chinese satellite Jilin-1, which is the first commercial satellite to offer multispectral night-light imagery at a spatial resolution below 1 m, to characterize the night-time properties of urban areas. We examined the correspondence between light intensities as measured from different sensors at different spatial resolutions: two Jilin-1 images of the Jerusalem metropolitan area (0.89 m), VIIRS/DNB (500 m), Loujia-1 (130 m), unmanned aerial vehicle (UAV) color image (0.05 m) and hemispherical color photographs taken by a calibrated ground DSLR (digital single-lens reflex camera). In all the comparisons between different remote sensing tools, as the spatial resolution coarsened, the Pearson correlation coefficient increased, reaching > 0.5 (after resampling to 100 m). Stronger correlations were found for the red band, and weaker correlations were found for the blue band, probably due to atmospheric scattering. By identifying specific objects such as buildings and lightings, we found good correspondence () between Jilin-1 and the ground-based measurements of night-time brightness. We further examined the variability of night lights within different land use types and within different ethnic/religion composition of statistical areas. We found that residential areas of Orthodox Jews were characterized with the highest brightness at night compared with residential areas of Arabs in the West Bank that had the lowest brightness. At the statistical zone level (n = 299), more than 50% of the variability in night-time brightness, was explained by land cover properties (NDVI), infrastructure (roads and built volume) and the ethnic/religious composition. In addition, we found that the spectral ratio index which was based on the red and green bands, enabled to better distinguish between land use classes, than the spectral ratio index which was based on the green and blue bands. The availability of night-time multi-spectral imagery at fine spatial resolution now enables to study urban land-use and spatial inequality, and to better understand the factors explaining night-time brightness.